Solver-Aided Verification of Policy Compliance in Tool-Augmented LLM Agents
arXiv:2603.20449v1 Announce Type: cross Abstract: Tool-augmented Large Language Models (TaLLMs) extend LLMs with the ability to invoke external tools, enabling…
Points-to-3D: Structure-Aware 3D Generation with Point Cloud Priors
arXiv:2603.18782v2 Announce Type: replace-cross Abstract: Recent progress in 3D generation has been driven largely by models conditioned on images or…
MOSS-TTSD: Text to Spoken Dialogue Generation
arXiv:2603.19739v1 Announce Type: cross Abstract: Spoken dialogue generation is crucial for applications like podcasts, dynamic commentary, and entertainment content, but…
Uncertainty-aware Prototype Learning with Variational Inference for Few-shot Point Cloud Segmentation
arXiv:2603.19757v1 Announce Type: cross Abstract: Few-shot 3D semantic segmentation aims to generate accurate semantic masks for query point clouds with…
CustomTex: High-fidelity Indoor Scene Texturing via Multi-Reference Customization
arXiv:2603.19121v2 Announce Type: replace-cross Abstract: The creation of high-fidelity, customizable 3D indoor scene textures remains a significant challenge. While text-driven…
When both Grounding and not Grounding are Bad — A Partially Grounded Encoding of Planning into SAT (Extended Version)
arXiv:2603.19429v1 Announce Type: new Abstract: Classical planning problems are typically defined using lifted first-order representations, which offer compactness and generality.…
Interpretability and implicit model semantics in biomedicine and deep learning
Nature Machine Intelligence, Published online: 23 March 2026; doi:10.1038/s42256-026-01177-0 We introduce a framework to analyse interpretability in deep learning, by…
Computational framework to predict and shape human–machine interactions in closed-loop, co-adaptive neural interfaces
Nature Machine Intelligence, Published online: 23 March 2026; doi:10.1038/s42256-026-01194-z Madduri et al. introduce a computational framework grounded in control and…
Multiscale Switch for Semi-Supervised and Contrastive Learning in Medical Ultrasound Image Segmentation
arXiv:2603.18655v1 Announce Type: cross Abstract: Medical ultrasound image segmentation faces significant challenges due to limited labeled data and characteristic imaging…